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X Demographics
Mendeley readers
Attention Score in Context
| Title |
A Deep Learning Approach for Mild Depression Recognition Based on Functional Connectivity Using Electroencephalography
|
|---|---|
| Published in |
Frontiers in Neuroscience, April 2020
|
| DOI | 10.3389/fnins.2020.00192 |
| Pubmed ID | |
| Authors |
Xiaowei Li, Rong La, Ying Wang, Bin Hu, Xuemin Zhang |
X Demographics
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Thailand | 1 | 17% |
| Unknown | 5 | 83% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 5 | 83% |
| Scientists | 1 | 17% |
Mendeley readers
The data shown below were compiled from readership statistics for 111 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 111 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 14 | 13% |
| Student > Master | 12 | 11% |
| Researcher | 8 | 7% |
| Student > Bachelor | 7 | 6% |
| Student > Doctoral Student | 6 | 5% |
| Other | 15 | 14% |
| Unknown | 49 | 44% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 15 | 14% |
| Engineering | 14 | 13% |
| Neuroscience | 10 | 9% |
| Psychology | 5 | 5% |
| Biochemistry, Genetics and Molecular Biology | 3 | 3% |
| Other | 8 | 7% |
| Unknown | 56 | 50% |
Attention Score in Context
This research output has an Altmetric Attention Score of 13. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 18 April 2020.
All research outputs
#2,841,627
of 25,387,668 outputs
Outputs from Frontiers in Neuroscience
#1,863
of 11,543 outputs
Outputs of similar age
#68,916
of 396,814 outputs
Outputs of similar age from Frontiers in Neuroscience
#108
of 341 outputs
Altmetric has tracked 25,387,668 research outputs across all sources so far. Compared to these this one has done well and is in the 88th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 11,543 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 11.0. This one has done well, scoring higher than 83% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 396,814 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 82% of its contemporaries.
We're also able to compare this research output to 341 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 67% of its contemporaries.